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Optimizing mold cooling channels using generative design
Even though additive manufacturing is moving beyond being a rapid prototyping technology, injection molding is still the most efficient way to mass produce plastics parts. Injection molding is therefore essential for mass-production of plastic parts with complex shapes. In order&
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Even though additive manufacturing is moving beyond being a rapid prototyping technology, injection molding is still the most efficient way to mass produce plastics parts. Injection molding is therefore essential for mass-production of plastic parts with complex shapes. In order to create higher quality products at lower costs, it is important to develop advanced cooling systems for melted materials. Panasonic Corporation’s Life Solutions Company, in collaboration with Autodesk, has explored how automating the design of mold cooling channels could help this process.
Therefore, Panasonic Corporation’s Life Solutions Company is betting on a new hybrid manufacturing method that incorporates 3D printing, milling and generative design. Using the LUMEX Avance-25, it was possible to create a conformal-cooling system (which lays out the cooling channels conforming to the shape of the products). The result is a reduction in cooling times by 20% compared to conventional methods, where channels are drilled straight through the mold.

Seiichi Uemoto, an analyst at Panasonic’s Life Solutions Company, initiated using generative design to deliver new designs that otherwise would have been impossible | Image via Autodesk
A workflow supported by generative design
Generative design is a technique that delivers optimized design outcomes based on goals and constraints. Seiichi Uemoto, an analyst at Panasonic’s Life Solutions Company Manufacturing Engineering Center, initiated using generative design to deliver new designs that otherwise would have been impossible. “I thought that with the right settings in place, I could use generative design to automatically create mold-cooling channels,” he explains. The main difference with topology optimization is that it produces “only one solution from the conditions provided to the system. It is difficult to produce something with smooth contours from the generated result. But it became apparent to me that generative design would inherently result in smoother shapes,” he adds.





